Take your first steps to become a fully qualified data analyst by learning how to explore large relational datasets
Key Features
Explore a variety of statistical techniques to analyze your data
Integrate your SQL pipelines with other analytics technologies
Perform advanced analytics such as geospatial and text analysis
Book DescriptionUnderstanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you.
SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation.
By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional.
Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
What you will learn
Perform advanced statistical calculations using the WINDOW function
Use SQL queries and subqueries to prepare data for analysis
Import and export data using a text file and psql
Apply special SQL clauses and functions to generate descriptive statistics
Analyze special data types in SQL, including geospatial data and time data
Optimize queries to improve their performance for faster results
Debug queries that won't run
Use SQL to summarize and identify patterns in data
Who this book is forIf you're a database engineer looking to transition into analytics, or a backend engineer who wants to develop a deeper understanding of production data, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL. Knowledge of basic SQL and database concepts will aid in understanding the concepts covered in this book.
Sprache
Verlagsort
Zielgruppe
Maße
Höhe: 235 mm
Breite: 191 mm
Dicke: 21 mm
Gewicht
ISBN-13
978-1-78980-735-6 (9781789807356)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Upom Malik is a data scientist who has worked in the technology industry for over 6 years. He has a master's degree in chemical engineering from Cornell University and a bachelor's degree in biochemistry from Duke University. He uses SQL and other tools to solve interesting challenges in finance, energy, and consumer technologies. While working on analytical problems, he has lived out of a suitcase and spent the last year as a digital nomad. Outside of work, he likes to read, hike the trails of the Northeastern United States, and savor ramen bowls from around the world. Matt Goldwasser is a lead data scientist at T. Rowe Price. He enjoys demystifying data science for business stakeholders and deploying production machine learning solutions. Matt has been using SQL to perform data analytics in the financial industry for the last 8 years. He has a bachelor's degree in mechanical and aerospace engineering from Cornell University. In his spare time, he enjoys teaching his infant son data science. Benjamin Johnston is a senior data scientist for one of the world's leading data-driven medtech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his PhD in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years' experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.
Table of Contents
Understanding and Describing Data
The Basics of SQL for Analytics
SQL for Data Preparation
Aggregate Functions for Data Analysis
Window Functions for Data Analysis
Importing and Exporting Data
Analytics Using Complex Data Types
Performant SQL
Using SQL to Uncover the Truth - A Case Study
